Systems Architect

OXMAN is seeking a System Architect to join the EDEN team as its first System Architect, owning the technical architecture across EDEN's platform, science and applications teams. The role sits at the intersection of systems engineering, scientific computing and applied AI. The System Architect will design and build the system that composes EDEN's models, runs closed-loop simulation and optimization at scale, makes sure their results can be trusted, and delivers them to users through a single interface. The role is hands-on and requires writing the first versions of critical pieces personally.

Responsibilities

Define the core abstractions, including the shared schema of traits, physical fields and world states, a common contract for operators and evaluators, and the representation of EDEN's procedural graphs; Architect the search, optimization and agentic control loop, including a harness that translates user intent into formal problem bounds with guardrails, and orchestration of parallel search, inner-loop tuning, evaluation budgets and diagnostic feedback; Design and build the runtime for physical simulation, learned surrogate models and long-running optimization at scale; Unify the prototypes onto a portable, scalable foundation so new scientific modules and application verticals plug in without custom glue; Make validation part of the architecture, including provenance, calibrated uncertainty, determinism, out-of-sample testing and physics-grounded checks; Design the data and delivery platform, including geospatial and ecological data ingestion, versioned site and run state, and backend services and APIs; Set engineering practice, including testing, reproducibility, CI and a clear line between research and production code

Requirements

Substantial experience designing and owning complex compute- or data-intensive systems, ideally in simulation, scientific computing, production ML, mathematical optimization or geospatial platforms; A track record of turning research prototypes into reliable, well-structured products; Strong systems design and Python expertise; Comfort with GPU workloads, parallel orchestration and cloud infrastructure; Expert-level Python; Runtime design for simulation and long-running optimization, including deterministic execution, content-aware caching and sandboxed code execution; Data platform design, including geospatial and ecological data ingestion, versioned state, backend services and APIs; Validation and reproducibility engineering, including provenance, calibrated uncertainty, determinism and out-of-sample testing; Engineering practice, including testing, CI and separating research code from production code

Nice to have

Experience with black-box, Bayesian or multi-objective optimization, quality-diversity search or reinforcement learning; Experience orchestrating and sandboxing LLM agents or program synthesis in production; Experience with surrogate modeling, differentiable simulation or GPU compute frameworks; Experience with procedural, DAG or node-based execution systems; Experience integrating with 3D web visualization, CAD/BIM or GIS software; An interest in ecology, earth observation or environmental modeling